
#Importing Libries
import numpy as np
import pandas as pd
import statistics as stats
import seaborn as sns
import matplotlib.pyplot as plt


#Import Dataset
dataset=pd.read_csv("C:\\Users\\Hrushikesh Patel\\Desktop\\p\\project\\weatherAUS.csv")
dataset


dataset.head()


dataset.shape

(145460, 23)


dataset.columns

Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',
       'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',
       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',
       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',
       'Temp3pm', 'RainToday', 'RainTomorrow'],
      dtype='object')


dataset.dtypes # every columns hava Series in pandas

Date              object
Location          object
MinTemp          float64
MaxTemp          float64
Rainfall         float64
Evaporation      float64
Sunshine         float64
WindGustDir       object
WindGustSpeed    float64
WindDir9am        object
WindDir3pm        object
WindSpeed9am     float64
WindSpeed3pm     float64
Humidity9am      float64
Humidity3pm      float64
Pressure9am      float64
Pressure3pm      float64
Cloud9am         float64
Cloud3pm         float64
Temp9am          float64
Temp3pm          float64
RainToday         object
RainTomorrow      object
dtype: object


dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 145460 entries, 0 to 145459
Data columns (total 23 columns):
 #   Column         Non-Null Count   Dtype  
---  ------         --------------   -----  
 0   Date           145460 non-null  object 
 1   Location       145460 non-null  object 
 2   MinTemp        143975 non-null  float64
 3   MaxTemp        144199 non-null  float64
 4   Rainfall       142199 non-null  float64
 5   Evaporation    82670 non-null   float64
 6   Sunshine       75625 non-null   float64
 7   WindGustDir    135134 non-null  object 
 8   WindGustSpeed  135197 non-null  float64
 9   WindDir9am     134894 non-null  object 
 10  WindDir3pm     141232 non-null  object 
 11  WindSpeed9am   143693 non-null  float64
 12  WindSpeed3pm   142398 non-null  float64
 13  Humidity9am    142806 non-null  float64
 14  Humidity3pm    140953 non-null  float64
 15  Pressure9am    130395 non-null  float64
 16  Pressure3pm    130432 non-null  float64
 17  Cloud9am       89572 non-null   float64
 18  Cloud3pm       86102 non-null   float64
 19  Temp9am        143693 non-null  float64
 20  Temp3pm        141851 non-null  float64
 21  RainToday      142199 non-null  object 
 22  RainTomorrow   142193 non-null  object 
dtypes: float64(16), object(7)
memory usage: 25.5+ MB


dataset.describe()


#DROP UNWANTED COLUMNS
drop = dataset.drop(['Evaporation','Sunshine'], axis=1)
drop.head(4)


#Rename Our Columns
renameData= dataset.rename(columns={'WindGustDir':'Wind_Gust_Dir',
                
                'WindGustSpeed':'Wind_Gust_Speed',
                'WindDir9am':"Wind_Dir_9am",
                'WindDir3pm':'Wind_Dir_3pm'
})
renameData.head()


renameData.columns

Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',
       'Sunshine', 'Wind_Gust_Dir', 'Wind_Gust_Speed', 'Wind_Dir_9am',
       'Wind_Dir_3pm', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am',
       'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm',
       'Temp9am', 'Temp3pm', 'RainToday', 'RainTomorrow'],
      dtype='object')


dataset.isna()


dataset.isna().sum()

Date                 0
Location             0
MinTemp           1485
MaxTemp           1261
Rainfall          3261
Evaporation      62790
Sunshine         69835
WindGustDir      10326
WindGustSpeed    10263
WindDir9am       10566
WindDir3pm        4228
WindSpeed9am      1767
WindSpeed3pm      3062
Humidity9am       2654
Humidity3pm       4507
Pressure9am      15065
Pressure3pm      15028
Cloud9am         55888
Cloud3pm         59358
Temp9am           1767
Temp3pm           3609
RainToday         3261
RainTomorrow      3267
dtype: int64


dataset.duplicated()

0         False
1         False
2         False
3         False
4         False
          ...  
145455    False
145456    False
145457    False
145458    False
145459    False
Length: 145460, dtype: bool


dataset.duplicated(subset=['Location'])

0         False
1          True
2          True
3          True
4          True
          ...  
145455     True
145456     True
145457     True
145458     True
145459     True
Length: 145460, dtype: bool


dataset.loc[dataset.duplicated(subset=['Location'])].head()


#checking Example duplicated
dataset.query ('Location == "Albury" ').head(6)


dataset['Date'].value_counts()

2013-11-12    49
2014-09-01    49
2014-08-23    49
2014-08-24    49
2014-08-25    49
              ..
2007-11-29     1
2007-11-28     1
2007-11-27     1
2007-11-26     1
2008-01-31     1
Name: Date, Length: 3436, dtype: int64


dataset['MaxTemp'].value_counts()

 20.0    885
 19.0    843
 19.8    840
 20.4    834
 19.9    823
        ... 
 46.5      1
 46.9      1
 47.0      1
 46.6      1
-2.4       1
Name: MaxTemp, Length: 505, dtype: int64


ax = dataset['MaxTemp'].value_counts() \
    .head(10) \
    .plot(kind='bar',color='r',title='Top 10 Maximum Tempareture')
ax.set_xlabel('<--Temparature-->')
ax.set_ylabel('<--Count-->')

Text(0, 0.5, '<--Count-->')


dataset['Humidity9am'].plot(kind='hist',bins=15,title= "Humidity in 9am")

<AxesSubplot:title={'center':'Humidity in 9am'}, ylabel='Frequency'>


dataset.plot(kind='scatter',
             x='MaxTemp',
             y='Humidity9am',color='c')

<AxesSubplot:xlabel='MaxTemp', ylabel='Humidity9am'>


dataset.plot(kind='scatter',
             x='MaxTemp',
             y='MinTemp',color='b')

<AxesSubplot:xlabel='MaxTemp', ylabel='MinTemp'>


dataset.plot(kind='scatter',
             x='MaxTemp',
             y='Pressure3pm',color='y')

<AxesSubplot:xlabel='MaxTemp', ylabel='Pressure3pm'>


sns.scatterplot(x='MaxTemp',
             y='Pressure3pm',data =dataset)

<AxesSubplot:xlabel='MaxTemp', ylabel='Pressure3pm'>


sns.scatterplot(x='MaxTemp',
             y='Pressure3pm',
            hue='MaxTemp',
            data =dataset)

<AxesSubplot:xlabel='MaxTemp', ylabel='Pressure3pm'>


#to see co-realation
df_corre = dataset[['MinTemp','MaxTemp','Rainfall','Humidity3pm','Pressure3pm']].dropna().corr()
df_corre


sns.heatmap(df_corre,annot=True)

<AxesSubplot:>


#Data Slicing of dependate values and independant values------here X is independent & y is dependented 
#And  here i have create 2D arry

x =dataset.iloc[:,[1,2,3,4,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]].values
x

array([['Albury', 13.4, 22.9, ..., 16.9, 21.8, 'No'],
       ['Albury', 7.4, 25.1, ..., 17.2, 24.3, 'No'],
       ['Albury', 12.9, 25.7, ..., 21.0, 23.2, 'No'],
       ...,
       ['Uluru', 5.4, 26.9, ..., 12.5, 26.1, 'No'],
       ['Uluru', 7.8, 27.0, ..., 15.1, 26.0, 'No'],
       ['Uluru', 14.9, nan, ..., 15.0, 20.9, 'No']], dtype=object)


y = dataset.iloc[:,-1].values
y

array(['No', 'No', 'No', ..., 'No', 'No', nan], dtype=object)


# this method use for change 1D to 2D arry
Y = y.reshape(-1,1) 
Y

array([['No'],
       ['No'],
       ['No'],
       ...,
       ['No'],
       ['No'],
       [nan]], dtype=object)


from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan,strategy ='most_frequent')
x=imputer.fit_transform(x)
Y=imputer.fit_transform(Y)
Y

array([['No'],
       ['No'],
       ['No'],
       ...,
       ['No'],
       ['No'],
       ['No']], dtype=object)

x

array([['Albury', 13.4, 22.9, ..., 16.9, 21.8, 'No'],
       ['Albury', 7.4, 25.1, ..., 17.2, 24.3, 'No'],
       ['Albury', 12.9, 25.7, ..., 21.0, 23.2, 'No'],
       ...,
       ['Uluru', 5.4, 26.9, ..., 12.5, 26.1, 'No'],
       ['Uluru', 7.8, 27.0, ..., 15.1, 26.0, 'No'],
       ['Uluru', 14.9, 20.0, ..., 15.0, 20.9, 'No']], dtype=object)


from sklearn.preprocessing import LabelEncoder

lel = LabelEncoder()
x[:,0] = lel.fit_transform(x[:,0])

lel2 =  LabelEncoder()
x[:,4] = lel2.fit_transform(x[:,4])

lel3 =  LabelEncoder()
x[:,6] = lel3.fit_transform(x[:,6])

lel4 =  LabelEncoder()
x[:,7] = lel4.fit_transform(x[:,4])

lel5 =  LabelEncoder()
x[:,-1] = lel5.fit_transform(x[:,-1])

lel6 =  LabelEncoder()
Y = lel6.fit_transform(Y)

C:\Users\Hrushikesh Patel\anaconda3\lib\site-packages\sklearn\preprocessing\_label.py:115: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

x

array([[2, 13.4, 22.9, ..., 16.9, 21.8, 0],
       [2, 7.4, 25.1, ..., 17.2, 24.3, 0],
       [2, 12.9, 25.7, ..., 21.0, 23.2, 0],
       ...,
       [41, 5.4, 26.9, ..., 12.5, 26.1, 0],
       [41, 7.8, 27.0, ..., 15.1, 26.0, 0],
       [41, 14.9, 20.0, ..., 15.0, 20.9, 0]], dtype=object)

Y

array([0, 0, 0, ..., 0, 0, 0])


from sklearn.preprocessing import StandardScaler


sc = StandardScaler()
x = sc.fit_transform(x)

x

array([[-1.53166617,  0.19132753, -0.04135977, ..., -0.01407077,
         0.02310362, -0.52979545],
       [-1.53166617, -0.75105231,  0.26874452, ...,  0.03244663,
         0.387799  , -0.52979545],
       [-1.53166617,  0.11279588,  0.35331842, ...,  0.62166712,
         0.22733303, -0.52979545],
       ...,
       [ 1.20928479, -1.06517892,  0.52246622, ..., -0.69632607,
         0.65037966, -0.52979545],
       [ 1.20928479, -0.68822699,  0.53656187, ..., -0.29317521,
         0.63579185, -0.52979545],
       [ 1.20928479,  0.42692249, -0.45013361, ..., -0.30868102,
        -0.10818671, -0.52979545]])


from sklearn.model_selection import train_test_split
x_train,x_test,Y_train,Y_test=train_test_split(x,Y,test_size = 0.20,random_state=0)
x_train

array([[ 0.22535368,  1.03946939,  0.07140543, ...,  0.68369032,
         0.08145488, -0.52979545],
       [ 1.42012717, -0.45263203,  0.11369237, ..., -0.41722163,
         0.22733303, -0.52979545],
       [ 0.50647685, -0.20133073, -0.14002932, ..., -0.06058818,
        -0.02065982,  1.88752093],
       ...,
       [ 1.0687232 ,  0.75675544,  0.93124006, ...,  1.10234698,
         1.07342629, -0.52979545],
       [ 0.57675765, -0.04426743, -0.16822062, ...,  0.01694083,
        -0.28324049,  1.88752093],
       [ 1.63096955, -0.0285611 , -0.91529006, ..., -0.35519842,
        -0.76463838, -0.52979545]])


from sklearn.ensemble import RandomForestClassifier
classifire = RandomForestClassifier(n_estimators=100,random_state=0)
classifire.fit(x_train,Y_train)

RandomForestClassifier(random_state=0)


# for checking how much score is accure of training dataset
classifire.score(x_train,Y_train)

0.9999226591502819


y_pre =classifire.predict(x_test)


y_pre

array([0, 0, 0, ..., 0, 0, 0])


from sklearn.metrics import accuracy_score
accuracy_check = accuracy_score( Y_test,y_pre)
accuracy_check

0.8538429808882166


from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
scores = cross_val_score(DecisionTreeClassifier(),x_train,Y_train, cv=5)


scores

array([0.77537166, 0.78014093, 0.77489903, 0.77712371, 0.77914321])


scores.mean() # this is also accurecy

0.777335707372336


from sklearn.linear_model import LogisticRegression


lr = LogisticRegression()


lr.fit(x_train,Y_train)

LogisticRegression()


lr.predict(x_test)

array([0, 0, 0, ..., 1, 0, 0])


lr.score(x_test,Y_test) #logistic regression accuaracy score is 84%

0.8426027773958477


from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures


poly_reg = PolynomialFeatures(degree=2)
poly_reg.fit(x_train)
X_train_poly = poly_reg.transform(x_train)
X_test_poly = poly_reg.transform(x_test)


X_train_poly.shape,X_test_poly.shape

((116368, 210), (29092, 210))


lr2 = LinearRegression()


lr2.fit(X_train_poly,Y_train)

LinearRegression()


lr2.score(X_test_poly,Y_test,)

0.34192818033542083


lr_pre = lr2.predict(X_test_poly)


lr2.score(X_test_poly,Y_test,)

0.34192818033542083


lr_pre = lr2.predict(X_test_poly)


lr_pre

array([0.0990449 , 0.28330241, 0.1391984 , ..., 0.4623271 , 0.23053751,
       0.04138956])


from sklearn.metrics import mean_squared_error
mse = mean_squared_error(Y_test,lr_pre)
rsme = np.sqrt(mse)


from pandas_profiling import ProfileReport


rp= ProfileReport(dataset)
rp

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
0	2008-12-01	Albury	13.4	22.9	0.6	NaN	NaN	W	44.0	W	...	71.0	22.0	1007.7	1007.1	8.0	NaN	16.9	21.8	No	No
1	2008-12-02	Albury	7.4	25.1	0.0	NaN	NaN	WNW	44.0	NNW	...	44.0	25.0	1010.6	1007.8	NaN	NaN	17.2	24.3	No	No
2	2008-12-03	Albury	12.9	25.7	0.0	NaN	NaN	WSW	46.0	W	...	38.0	30.0	1007.6	1008.7	NaN	2.0	21.0	23.2	No	No
3	2008-12-04	Albury	9.2	28.0	0.0	NaN	NaN	NE	24.0	SE	...	45.0	16.0	1017.6	1012.8	NaN	NaN	18.1	26.5	No	No
4	2008-12-05	Albury	17.5	32.3	1.0	NaN	NaN	W	41.0	ENE	...	82.0	33.0	1010.8	1006.0	7.0	8.0	17.8	29.7	No	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
145455	2017-06-21	Uluru	2.8	23.4	0.0	NaN	NaN	E	31.0	SE	...	51.0	24.0	1024.6	1020.3	NaN	NaN	10.1	22.4	No	No
145456	2017-06-22	Uluru	3.6	25.3	0.0	NaN	NaN	NNW	22.0	SE	...	56.0	21.0	1023.5	1019.1	NaN	NaN	10.9	24.5	No	No
145457	2017-06-23	Uluru	5.4	26.9	0.0	NaN	NaN	N	37.0	SE	...	53.0	24.0	1021.0	1016.8	NaN	NaN	12.5	26.1	No	No
145458	2017-06-24	Uluru	7.8	27.0	0.0	NaN	NaN	SE	28.0	SSE	...	51.0	24.0	1019.4	1016.5	3.0	2.0	15.1	26.0	No	No
145459	2017-06-25	Uluru	14.9	NaN	0.0	NaN	NaN	NaN	NaN	ESE	...	62.0	36.0	1020.2	1017.9	8.0	8.0	15.0	20.9	No	NaN

	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
0	2008-12-01	Albury	13.4	22.9	0.6	NaN	NaN	W	44.0	W	...	71.0	22.0	1007.7	1007.1	8.0	NaN	16.9	21.8	No	No
1	2008-12-02	Albury	7.4	25.1	0.0	NaN	NaN	WNW	44.0	NNW	...	44.0	25.0	1010.6	1007.8	NaN	NaN	17.2	24.3	No	No
2	2008-12-03	Albury	12.9	25.7	0.0	NaN	NaN	WSW	46.0	W	...	38.0	30.0	1007.6	1008.7	NaN	2.0	21.0	23.2	No	No
3	2008-12-04	Albury	9.2	28.0	0.0	NaN	NaN	NE	24.0	SE	...	45.0	16.0	1017.6	1012.8	NaN	NaN	18.1	26.5	No	No
4	2008-12-05	Albury	17.5	32.3	1.0	NaN	NaN	W	41.0	ENE	...	82.0	33.0	1010.8	1006.0	7.0	8.0	17.8	29.7	No	No

	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustSpeed	WindSpeed9am	WindSpeed3pm	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm
count	143975.000000	144199.000000	142199.000000	82670.000000	75625.000000	135197.000000	143693.000000	142398.000000	142806.000000	140953.000000	130395.00000	130432.000000	89572.000000	86102.000000	143693.000000	141851.00000
mean	12.194034	23.221348	2.360918	5.468232	7.611178	40.035230	14.043426	18.662657	68.880831	51.539116	1017.64994	1015.255889	4.447461	4.509930	16.990631	21.68339
std	6.398495	7.119049	8.478060	4.193704	3.785483	13.607062	8.915375	8.809800	19.029164	20.795902	7.10653	7.037414	2.887159	2.720357	6.488753	6.93665
min	-8.500000	-4.800000	0.000000	0.000000	0.000000	6.000000	0.000000	0.000000	0.000000	0.000000	980.50000	977.100000	0.000000	0.000000	-7.200000	-5.40000
25%	7.600000	17.900000	0.000000	2.600000	4.800000	31.000000	7.000000	13.000000	57.000000	37.000000	1012.90000	1010.400000	1.000000	2.000000	12.300000	16.60000
50%	12.000000	22.600000	0.000000	4.800000	8.400000	39.000000	13.000000	19.000000	70.000000	52.000000	1017.60000	1015.200000	5.000000	5.000000	16.700000	21.10000
75%	16.900000	28.200000	0.800000	7.400000	10.600000	48.000000	19.000000	24.000000	83.000000	66.000000	1022.40000	1020.000000	7.000000	7.000000	21.600000	26.40000
max	33.900000	48.100000	371.000000	145.000000	14.500000	135.000000	130.000000	87.000000	100.000000	100.000000	1041.00000	1039.600000	9.000000	9.000000	40.200000	46.70000

	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	Wind_Gust_Dir	Wind_Gust_Speed	Wind_Dir_9am	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
0	2008-12-01	Albury	13.4	22.9	0.6	NaN	NaN	W	44.0	W	...	71.0	22.0	1007.7	1007.1	8.0	NaN	16.9	21.8	No	No
1	2008-12-02	Albury	7.4	25.1	0.0	NaN	NaN	WNW	44.0	NNW	...	44.0	25.0	1010.6	1007.8	NaN	NaN	17.2	24.3	No	No
2	2008-12-03	Albury	12.9	25.7	0.0	NaN	NaN	WSW	46.0	W	...	38.0	30.0	1007.6	1008.7	NaN	2.0	21.0	23.2	No	No
3	2008-12-04	Albury	9.2	28.0	0.0	NaN	NaN	NE	24.0	SE	...	45.0	16.0	1017.6	1012.8	NaN	NaN	18.1	26.5	No	No
4	2008-12-05	Albury	17.5	32.3	1.0	NaN	NaN	W	41.0	ENE	...	82.0	33.0	1010.8	1006.0	7.0	8.0	17.8	29.7	No	No

	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
1	2008-12-02	Albury	7.4	25.1	0.0	NaN	NaN	WNW	44.0	NNW	...	44.0	25.0	1010.6	1007.8	NaN	NaN	17.2	24.3	No	No
2	2008-12-03	Albury	12.9	25.7	0.0	NaN	NaN	WSW	46.0	W	...	38.0	30.0	1007.6	1008.7	NaN	2.0	21.0	23.2	No	No
3	2008-12-04	Albury	9.2	28.0	0.0	NaN	NaN	NE	24.0	SE	...	45.0	16.0	1017.6	1012.8	NaN	NaN	18.1	26.5	No	No
4	2008-12-05	Albury	17.5	32.3	1.0	NaN	NaN	W	41.0	ENE	...	82.0	33.0	1010.8	1006.0	7.0	8.0	17.8	29.7	No	No
5	2008-12-06	Albury	14.6	29.7	0.2	NaN	NaN	WNW	56.0	W	...	55.0	23.0	1009.2	1005.4	NaN	NaN	20.6	28.9	No	No

First Model- Random Forest Model¶

FIRST MODEL RANDOM FOREST ACCURACY¶

Second Model Dession Tree¶

third model LogrsticRegression¶

fourth model POLYNOMIAL FEATURES¶

conclusion:¶

THANK YOU¶

	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
0	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	False	True	False	False	False	False
1	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	True	False	False	False	False
2	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	False	False	False	False	False
3	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	True	False	False	False	False
4	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
145455	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	True	False	False	False	False
145456	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	True	False	False	False	False
145457	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	True	True	False	False	False	False
145458	False	False	False	False	False	True	True	False	False	False	...	False	False	False	False	False	False	False	False	False	False
145459	False	False	False	True	False	True	True	True	True	False	...	False	False	False	False	False	False	False	False	False	True

	MinTemp	MaxTemp	Rainfall	Humidity3pm	Pressure3pm
MinTemp	1.000000	0.727119	0.109141	0.028305	-0.458110
MaxTemp	0.727119	1.000000	-0.070787	-0.495877	-0.422165
Rainfall	0.109141	-0.070787	1.000000	0.253789	-0.125554
Humidity3pm	0.028305	-0.495877	0.253789	1.000000	0.051285
Pressure3pm	-0.458110	-0.422165	-0.125554	0.051285	1.000000